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Appl. Sci. 2018, 8(9), 1621; https://doi.org/10.3390/app8091621

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 14 August 2018 / Revised: 4 September 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Abstract

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings. View Full-Text
Keywords: feature fusion; sensitive IMFs selection; fault diagnosis; EEMD; SVM feature fusion; sensitive IMFs selection; fault diagnosis; EEMD; SVM
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Jiang, F.; Zhu, Z.; Li, W.; Ren, Y.; Zhou, G.; Chang, Y. A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis. Appl. Sci. 2018, 8, 1621.

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